Cervical cell nuclei segmentation based on GC-UNet Article Swipe

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Enguang Zhang , Rixin Xie , Yuxin Bian , Jiayan Wang , Pengyi Tao , H.Y. Zhang , Shenlu Jiang ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1016/j.heliyon.2023.e17647

Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.heliyon.2023.e17647
PDF: http://www.cell.com/article/S2405844023048557/pdf
OA Status: gold
Cited By: 11
References: 54
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DOI: https://doi.org/10.1016/j.heliyon.2023.e17647

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Title: Cervical cell nuclei segmentation based on GC-UNet

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Language: en

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Publication year: 2023

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Publication date: 2023-06-28

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Authors: Enguang Zhang, Rixin Xie, Yuxin Bian, Jiayan Wang, Pengyi Tao, H.Y. Zhang, Shenlu Jiang

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Landing page: https://doi.org/10.1016/j.heliyon.2023.e17647

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Concepts: Segmentation, Artificial intelligence, Chemistry, Computer science

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Cited by: 11

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